Predictive maintenance is a strategy for servicing machines based on their actual condition rather than on a fixed calendar or usage schedule. It uses condition- monitoring data - vibration, temperature, acoustic emissions, oil analysis, and other sensor signals - together with machine learning to estimate when a piece of equipment is likely to fail, so it can be repaired just in time. This contrasts with preventive maintenance, which services equipment on a rigid schedule and therefore both wastes effort on healthy machines and misses ones that degrade between intervals.
A central technical problem is estimating remaining useful life: how many more operating cycles or hours a machine has before failure. Research on this matured around shared benchmarks such as NASA’s C-MAPSS turbofan dataset, introduced in the 2008 prognostics paper by Saxena, Goebel, Simon, and Eklund, which provided simulated run-to-failure sensor data so different prediction methods could be compared. Modern systems often pair such models with a digital twin of the asset and a continuous stream of IoT data.
For a business reader, the appeal is straightforward: catching a failure before it happens avoids unplanned downtime, which is usually far more expensive than the repair itself, and it is one of the most widely deployed real-world uses of AI in heavy industry.